Cosmology Group

Forecasting

Grey: Likelihoods as predicted by a slow grid evaluation.
Blue: Contours as gained with DALI, about 1000 times faster than the
grid evaluation. arxiv:1401.6892

Theoretical physics poses questions about nature that have to be
answered by taking data. However, building and conducting an
experiment is a tedious and painstaking effort and often practical
necessities enforce decisions about the design. Theoreticians can
optimize these decisions by forecasting which experimental design
will ultimately lead to the most constraining data.

Additionally, building the most precise experiment, doesn't
automatically guarantee the most precise answers to the questions
one has asked: The data also have to be analysed with a precision
similar to that of the actual measurements. However, if the
analysis shall be executable in less than a Hubble time, then
idealizations are often mandatory. Forecasting tools are also
helpful here: They can be used to asess into which analysis steps
much computational effort should be investigated and when an
idealizations is enough in order to make optimal use of the new data.

In our working groups, we test and extend the current standard model
of cosmology, with the aim of finding detailed and satisfying
answers to the problems of dark energy and dark matter. As such, we
often predict hitherto unobserved phenomena that might appear if our
universe is described by another model than the current
LambdaCDM-cosmology. These new predictions will only be observable
with future data – we therefore help to optimize experiments like
the ESA-satellite Euclid.

Naturally, predicting new phenomena introduces new parameters to the
model of our universe. These new parameters are often at best
loosely constrained by current data – from a statistical point of
view this means that their likelihoods are often decidedly
non-gaussian. We therefore developed a new forecasting tool, DALI ( see arxiv:1401.6892
and image left), that is able to deal with non-Gaussian
likelihoods. With this tool, we can safely go beyond the well
sampled parameter space of the standard model and out into the sofar
sparsely sampled parameter space of new physics – hopefully to
convince observational astronomers to concentrate their efforts on
these new spaces.